Forecast of Seaport Cargo Volume Based on Artificial Neural Network Model
Publication: ICLEM 2010: Logistics For Sustained Economic Development: Infrastructure, Information, Integration
Abstract
In this paper two artificial neural network models were explored to predict future cargo volume of Dalian seaport. Factors affecting cargo volume were carefully identified, categorized as input and output variables and then entered into the forecast models that generated a projection of the cargo volume. First we used time series methods predicting the future results of the input variables. Then we utilized a radial base function neural network as the basic model. Finally, we combined this radial base neural network and a linear function to be a generalized regression neural network, which generated the results of the cargo volume. The results indicate that the Dalian seaport cargo volume will increase in the near future and local economics that depends heavily on sea transportation will improve rapidly.
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© 2010 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Engineering fundamentals
- Forecasting
- Hydraulic engineering
- Hydraulic structures
- Linear functions
- Mathematical functions
- Mathematics
- Models (by type)
- Neural networks
- Optimization models
- Ports and harbors
- Project management
- Regression analysis
- Statistical analysis (by type)
- Statistics
- Time series analysis
- Water and water resources
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